app.py

#!/usr/bin/env python
"""Demo app for https://github.com/adobe-research/custom-diffusion.
The code in this repo is partly adapted from the following repository:
https://huggingface.co/spaces/hysts/LoRA-SD-training
MIT License
Copyright (c) 2022 hysts
==========================================================================================
Adobe’s modifications are Copyright 2022 Adobe Research. All rights reserved.
Adobe’s modifications are licensed under the Adobe Research License. To view a copy of the license, visit
LICENSE.
==========================================================================================
"""

from __future__ import annotations
import sys
import os
import pathlib

import gradio as gr
import torch

from inference import inference_fn
# from inference_custom_diffusion import InferencePipeline
# from trainer import Trainer
# from uploader import upload

TITLE = '# Custom Diffusion + StableDiffusion Training UI'
DESCRIPTION = '''This is a demo for [https://github.com/adobe-research/custom-diffusion](https://github.com/adobe-research/custom-diffusion).
It is recommended to upgrade to GPU in Settings after duplicating this space to use it.
<a href="https://huggingface.co/spaces/nupurkmr9/custom-diffusion?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
'''
DETAILDESCRIPTION='''
Custom Diffusion allows you to fine-tune text-to-image diffusion models, such as Stable Diffusion, given a few images of a new concept (~4-20).
We fine-tune only a subset of model parameters, namely key and value projection matrices, in the cross-attention layers and the modifier token used to represent the object.
This also reduces the extra storage for each additional concept to 75MB. Our method also allows you to use a combination of concepts. There's still limitations on which compositions work. For more analysis please refer to our [website](https://www.cs.cmu.edu/~custom-diffusion/).
<center>
<img src="https://huggingface.co/spaces/nupurkmr9/custom-diffusion/resolve/main/method.jpg" width="600" align="center" >
</center>
'''

ORIGINAL_SPACE_ID = 'Ziqi/ReVersion'
SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
SHARED_UI_WARNING = f'''# Attention - This Space doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
'''
if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'

else:
    SETTINGS = 'Settings'
CUDA_NOT_AVAILABLE_WARNING = f'''# Attention - Running on CPU.
<center>
You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
"T4 small" is sufficient to run this demo.
</center>
'''

os.system("git clone https://github.com/ziqihuangg/ReVersion")
sys.path.append("ReVersion")

def show_warning(warning_text: str) -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Box():
            gr.Markdown(warning_text)
    return demo


def update_output_files() -> dict:
    paths = sorted(pathlib.Path('results').glob('*.bin'))
    paths = [path.as_posix() for path in paths]  # type: ignore
    return gr.update(value=paths or None)

def find_weight_files() -> list[str]:
    curr_dir = pathlib.Path(__file__).parent
    paths = sorted(curr_dir.rglob('*.bin'))
    paths = [path for path in paths if '.lfs' not in str(path)]
    return [path.relative_to(curr_dir).as_posix() for path in paths]


def reload_custom_diffusion_weight_list() -> dict:
    return gr.update(choices=find_weight_files())


def create_inference_demo(func: inference_fn) -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                model_id = gr.Dropdown(
                    choices=['experiments/painted_on'],
                    value='experiments/painted_on',
                    label='Relation',
                    visible=True)
                reload_button = gr.Button('Reload Weight List')
                prompt = gr.Textbox(
                    label='Prompt',
                    max_lines=1,
                    placeholder='Example: "cat <R> stone"')
                placeholder_string = gr.Textbox(
                    label='Placeholder String',
                    max_lines=1,
                    placeholder='Example: "<R>"')

                with gr.Accordion('Other Parameters', open=False):
                    guidance_scale = gr.Slider(label='Classifier-Free Guidance Scale',
                                               minimum=0,
                                               maximum=50,
                                               step=0.1,
                                               value=7.5)
                    num_samples = gr.Slider(label='Batch Size',
                                               minimum=0,
                                               maximum=10.,
                                               step=1,
                                               value=10)

                run_button = gr.Button('Generate')

                gr.Markdown('''
                - Models with names starting with "custom-diffusion-models/" are the pretrained models provided in the [original repo](https://github.com/adobe-research/custom-diffusion), and the ones with names starting with "results/delta.bin" are your trained models.
                - After training, you can press "Reload Weight List" button to load your trained model names.
                - Increase number of steps in Other parameters for better samples qualitatively.
                ''')
            with gr.Column():
                result = gr.Image(label='Result')

        # reload_button.click(fn=reload_custom_diffusion_weight_list,
        #                     inputs=None,
        #                     outputs=weight_name)
        prompt.submit(fn=func,
                      inputs=[
                          model_id,
                          prompt,
                          placeholder_string,
                          guidance_scale
                      ],
                      outputs=result,
                      queue=False)
        run_button.click(fn=func,
                        inputs=[
                            model_id,
                            prompt,
                            placeholder_string,
                            guidance_scale
                        ],
                         outputs=result,
                         queue=False)
    return demo


with gr.Blocks(css='style.css') as demo:
    if os.getenv('IS_SHARED_UI'):
        show_warning(SHARED_UI_WARNING)
    if not torch.cuda.is_available():
        show_warning(CUDA_NOT_AVAILABLE_WARNING)

    gr.Markdown(TITLE)
    gr.Markdown(DESCRIPTION)
    gr.Markdown(DETAILDESCRIPTION)

    with gr.Tabs():

        with gr.TabItem('Test'):
            create_inference_demo(inference_fn)


demo.queue(default_enabled=False).launch(share=False)